Knowledge workers comprise a large proportion of the workforce today employed in the context of large-scale expert networks, spanning across different disciplines and organizations. They rely on software technologies for their discipline-specific activities, and for their coordination and their communication with each other. This type of networked, interdisciplinary knowledge work is motivated by a belief that the complex problems of our time can only be effectively addressed through broad collaborations of experts and partners who act both as knowledge generators and receptors to create value through new products and services. However, even as this belief is generally, and increasingly, shared, the question of “what are the properties of effective knowledge networks and how can they be supported to become more effective” is still open and the subject of considerable debate.
The goal of this project is to develop methods and tools for analyzing and enhancing the data sets through which knowledge workers collaborate, our hypothesis being that, by doing so, we can effectively amplify their productivity. These methods and tools will be validated through case studies with our partner organizations: an enterprise research and development (R&D) organization (Dell), two Canadian SMEs, one offering an expert-finder service (Expertise Finder) and the other, a platform for biomedical research (ScienceScape), and two Canadian research networks (the newly funded AGE-WELL and GlycoNET NCEs). We will demonstrate how our tools can enhance innovation productivity, and help mobilize the work products of our partners towards adoption and value creation.
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